Estimating effort is a very important task in any organization. Significant over or under-estimates can be very expensive for software project companies. The use of computing intelligence methods has been recently pro...
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ISBN:
(纸本)9781538630570
Estimating effort is a very important task in any organization. Significant over or under-estimates can be very expensive for software project companies. The use of computing intelligence methods has been recently proposed for software development effort estimation. In this study, we present new models to estimate the effort required for the development of software projects. These new models were calculated using linear genetic programming (PGL). The results show that the proposed models get more precise and more effective estimation for Mean Magnitude Relative error (MMRE) and Mean Magnitude of Relative Error relative to the Estimate (MMER) than using the constructive cost model (COCOMO). We performed the study based on three stages according to the type of project. The models were designed and validated by simulation with the public repository dataset COCOMO81 and NASA93. Performance of the proposed models EE_PGL(A), EE_PGL(B) and EE_PGL(C) are more accurate than COCOMO.
Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. Deciphering what genes are associated with a specific disease/disorder helps better d...
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ISBN:
(数字)9783030440947
ISBN:
(纸本)9783030440930;9783030440947
Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. Deciphering what genes are associated with a specific disease/disorder helps better diagnose and treat it, and may even prevent it if predicted accurately and acted on effectively at early stages. The evaluation of candidate disease-associated genes, however, requires time-consuming and expensive experiments given the large number of possibilities. Due to such challenges, computational methods have seen increasing applications in predicting gene-disease associations. Given the intertwined relationships of molecules in human cells, genes and their products can be considered to form a complex molecular interaction network. Such a network can be used to find candidate genes that share similar network properties with known disease-associated genes. In this research, we investigate autism spectrum disorders and propose a linear genetic programming algorithm for autism gene prediction using a human molecular interaction network and known autism-genes for training. We select an initial set of network properties as features and our LGP algorithm is able to find the most relevant features while evolving accurate predictive models. Our research demonstrates the powerful and flexible learning abilities of GP on tackling a significant biomedical problem, and is expected to inspire further exploration of wide GP applications.
Cardiac rhythm disorders may cause severe heart diseases, stroke, and even sudden cardiac death. Some arrhythmias are so serious that can cause injury to other organs, for instance, brain, kidneys, lungs or liver. The...
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ISBN:
(纸本)9781450342063
Cardiac rhythm disorders may cause severe heart diseases, stroke, and even sudden cardiac death. Some arrhythmias are so serious that can cause injury to other organs, for instance, brain, kidneys, lungs or liver. Therefore, early and correct diagnosis of cardiac arrhythmia is essential to the prevention of serious problems. There are expert systems to classify arrhythmias from electrocardiograms signals. However, it has been shown that not only selecting the correct features from the dataset but also generating combined features could be the key to having real progress in classification. Therefore, this paper investigates a novel hybrid evolutionary technique to perform both tasks at the same time, finding complementary features that cover different characteristics of the data. The new features were tested with a widely-used classifier called Random Forests. The method reduced a dataset with 279 attributes to 26 attributes and achieved accuracies of 86.39% for binary classification and 77.69% for multiclass. Our approach outperformed several popular feature selection, feature generation, and state-of-the-art related work from the literature.
Using geneticprogramming-based hyper-heuristic methods to automatically design dispatching rules has become one of the most effective methods to solve dynamic job shop scheduling. However, most geneticprogramming-ba...
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ISBN:
(纸本)9781728190488
Using geneticprogramming-based hyper-heuristic methods to automatically design dispatching rules has become one of the most effective methods to solve dynamic job shop scheduling. However, most geneticprogramming-based hyper-heuristic methods are developed based on tree-like structures. On the other hand, linear genetic programming variants, whose individuals are designed in a linear fashion, also have been successfully applied to some classification and symbolic regression problems and achieved promising results. But the studies of linear genetic programming as a hyper-heuristic for evolving dispatching rules for job shop scheduling are still in the infancy. To apply linear genetic programming to dynamic job shop scheduling (DJSS), this paper makes a comprehensive investigation on the design issues of linear genetic programming (e.g., the number of registers and the variation step size) and validates that linear genetic programming has a competitive performance with standard geneticprogramming and can produce compact dispatching rules.
This research examines the optimization of decision tree induction techniques by integrating evolutionary algorithms. It focuses on the linear genetic programming Decision Tree (LGPDT). LGPDT employs a linear program ...
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ISBN:
(纸本)9798350373981;9798350373974
This research examines the optimization of decision tree induction techniques by integrating evolutionary algorithms. It focuses on the linear genetic programming Decision Tree (LGPDT). LGPDT employs a linear program to encode decision trees, achieving an optimal balance between accuracy and interpretability. The study introduces C-LGPDT as an extension of LGPDT, aiming to enhance its efficiency through correlation-based feature selection. This integration reduces dataset dimensionality and eliminates irrelevant or redundant features, resulting in a more accurate and interpretable decision tree model. The performance of C-LGPDT is thoroughly examined, and it is shown that it consistently outperforms older approaches, especially C4.5, and that it is more robust and accurate. A tourism dataset is also used to evaluate the C-LGPDT's performance, with an emphasis on its stability in recall and precision. Results show that C-LGPDT is effective at solving decision tree induction problems, making it a good candidate for machine learning classification tasks.
Dynamic Job Shop Scheduling (DJSS) is an important problem with many real-world applications. geneticprogramming is a promising technique to solve DJSS, which automatically evolves dispatching rules to make real-time...
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ISBN:
(纸本)9781665487689
Dynamic Job Shop Scheduling (DJSS) is an important problem with many real-world applications. geneticprogramming is a promising technique to solve DJSS, which automatically evolves dispatching rules to make real-time scheduling decisions in dynamic environments. linear genetic programming (LGP) is a notable variant of geneticprogramming methods. Compared with Tree-based geneticprogramming (TGP), LGP has high flexibility of reusing building blocks and easy control of bloat effect. Due to these advantages, LGP has been successfully applied to various domains such as classification and symbolic regression. However, for solving DJSS, the most commonly used GP method is TGP. It is interesting to see whether LGP can perform well, or even outperform TGP in the DJSS domain. Applying LGP as a hyper-heuristic method to solve DJSS problems is still in its infancy. An existing study has investigated some basic design issues (e.g., parameter sensitivity and training and test performance) of LGP. However, that study lacks a comprehensive investigation on the number of generations and different genetic operator rates, and misses the investigation on register initialization strategy of LGP. To have a more comprehensive investigation, this paper investigates different generations, genetic operator rates, and register initialization strategies of LGP for solving DJSS. A further comparison with TGP is also conducted. The results show that sufficient evolution generations and initializing registers by diverse features are important for LGP to have a superior performance.
Dispatching rules are commonly used to make instant decisions in dynamic scheduling problems. linear genetic programming (LGP) is one of the effective methods to design dispatching rules automatically. However, the ef...
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ISBN:
(纸本)9798400701191
Dispatching rules are commonly used to make instant decisions in dynamic scheduling problems. linear genetic programming (LGP) is one of the effective methods to design dispatching rules automatically. However, the effectiveness and efficiency of LGP methods are limited due to the large search space. Exploring the entire search space of programs is inefficient for LGP since a large number of programs might contain redundant blocks and might be inconsistent with domain knowledge, which would further limit the effectiveness of the produced LGP models. To improve the performance of LGP in dynamic job shop scheduling problems, this paper proposes a grammar-guided LGP to make LGP focus more on promising programs. Our dynamic job shop scheduling simulation results show that the proposed grammar-guided LGP has better training efficiency than basic LGP, and can produce solutions with good explanations. Further analyses show that grammar-guided LGP significantly improves the overall test effectiveness when the number of LGP registers increases.
A developmental co-evolutionary genetic progamming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors. Both implementations were fo...
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ISBN:
(纸本)9783642011283
A developmental co-evolutionary genetic progamming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors. Both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit, where PAM DGP proved slightly more reactive to market changes than LGP. PAM DGP outperformed, or was competitive with, LGP for all stocks tested. Both implementations had very impressive accuracy in choosing both profitable buy trades and sells that prevented losses, where this occurred in the context of moderately active trading for all stocks. The algorithms also appropriately maintained maximal investment in order to profit from Sustained market upswings.
In linear variants of geneticprogramming (GP) like linear genetic programming (LGP), structural introns can emerge, which are nodes that are not connected to the final output and do not contribute to the output of a ...
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ISBN:
(纸本)9781450361118
In linear variants of geneticprogramming (GP) like linear genetic programming (LGP), structural introns can emerge, which are nodes that are not connected to the final output and do not contribute to the output of a program. There are claims that such non-effective code is beneficial for search, as it can store relevant and important evolved information that can be reactivated in later search phases. Furthermore, introns can increase diversity, which leads to higher GP performance. This paper studies the role of non-effective code by comparing the performance of LGP variants that deal differently with non-effective code for standard symbolic regression problems. As we find no decrease in performance when removing or randomizing structural introns in each generation of a LGP run, we have to reject the hypothesis that structural introns increase LGP performance by preserving meaningful sub-structures. Our results indicate that there is no important information stored in structural introns. In contrast, we find evidence that the increase of diversity due to structural introns positively affects LGP performance.
Defining a distance measure over the individuals in the population of an Evolutionary Algorithm can be exploited for several applications, ranging from diversity preservation to balancing exploration and exploitation....
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ISBN:
(纸本)9781450319638
Defining a distance measure over the individuals in the population of an Evolutionary Algorithm can be exploited for several applications, ranging from diversity preservation to balancing exploration and exploitation. When individuals are encoded as strings of bits or sets of real values, computing the distance between any two can be a straightforward process;when individuals are represented as trees or linear graphs, however, quite often the user must resort to phenotype-level problem-specific distance metrics. This paper presents a generic genotype-level distance metric for linear genetic programming: the information contained by an individual is represented as a set of symbols, using n-grams to capture significant recurring structures inside the genome. The difference in information between two individuals is evaluated resorting to a symmetric difference. Experimental evaluations show that the proposed metric has a strong correlation with phenotype-level problem-specific distance measures in two problems where individuals represent string of bits and Assembly-language programs, respectively.
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